Julia Scientific Programming
Learn Julia programming for scientific computing: data analysis, visualization, and modeling with hands-on projects.
Learn Julia programming for scientific computing: data analysis, visualization, and modeling with hands-on projects.
This course introduces Julia as a first programming language for scientific computing. Students will learn the basics of Julia syntax, data structures, and programming concepts while applying them to real-world scientific problems. The course covers data manipulation, visualization with Plots, statistical analysis, and modeling techniques like SIR models for epidemiology. Through hands-on projects and case studies, learners will gain practical experience in using Julia for data analysis, curve fitting, and creating publication-quality visualizations. Suitable for beginners, the course emphasizes Julia's high-performance capabilities and its applications in various scientific fields.
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English
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What you'll learn
How to start coding in the Julia programming language
The advantages and capacities of Julia as a computing language
Programme using the Julia language by practicing through assignments
Write your own simple Julia programs from scratch
Work in Jupyter notebooks using the Julia language
Use various Julia packages such as Plots, DataFrames and Stats
Skills you'll gain
This course includes:
356 Minutes PreRecorded video
15 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to Julia programming for scientific computing. Students will learn the fundamentals of Julia syntax, data structures, and control flow while applying these concepts to real-world scientific problems. The curriculum covers essential topics such as data manipulation, visualization using the Plots package, statistical analysis, and modeling techniques. Learners will work with real datasets, including a case study on the Ebola epidemic, to understand how to load, process, and analyze data in Julia. The course also introduces more advanced concepts like SIR models for epidemiology and explores Julia's powerful packages for data science, including DataFrames and Distributions. Through a combination of video lectures, hands-on coding exercises, and peer-reviewed projects, students will gain practical experience in using Julia for various scientific computing tasks, from basic data analysis to more complex modeling and visualization.
Welcome to the course
Module 1 · 7 Hours to complete
A context for exploring Julia: Working with data
Module 2 · 3 Hours to complete
Notebooks as Julia Programs
Module 3 · 3 Hours to complete
Structuring data and functions in Julia
Module 4 · 4 Hours to complete
Fee Structure
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Instructors
Advancing Plant Ecology Through Mathematical Insights at UCT
Dr. Henri Laurie is a senior lecturer in Mathematics and Applied Mathematics at the University of Cape Town, where he also teaches Applied Mathematics. His primary research interest is leveraging mathematical techniques to enhance discussions in plant ecology. His work encompasses various topics, including pattern analysis, the effects of size distribution, lottery models for Cape fynbos, heuweltjies, projective geometry related to human motion, and multifractal patterns in nature. Recently, he has been utilizing the Julia programming language to implement models of plant-herbivore dynamics in savanna ecosystems.
Advancing Emergency Surgery and Education at Groote Schuur Hospital
Dr. Juan Klopper is a surgeon in academic practice at Groote Schuur Hospital and serves as a senior lecturer in surgery at the University of Cape Town. After working in private practice, he returned to academia, where he now heads the Acute Care Surgery unit, specializing in emergency surgical conditions. In addition to his clinical responsibilities, Dr. Klopper creates YouTube videos on physics, mathematics, and surgery, embodying his mission statement: "Knowledge belongs to us all. Never stop learning."
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4.4 course rating
428 ratings
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